The present embodiments relate to nuclear medical imaging. Example nuclear imaging modalities include single photon emission computed tomography (SPECT) and positron emission tomography (PET). A radioactive substance is administered to a patient. An imaging detector detects the γ-radiation emitted from the patient. The detected emissions are tomographically reconstructed to generate an image object of locations of the emissions in a patient. Due to respiration motion during detection, the reconstructed image object may include motion artifacts or be blurred.
In SPECT imaging, respiratory motion causes blurring and artifacts in reconstructed images due to projection durations that approach or exceed the length of a respiratory cycle. If a respiratory surrogate signal is available that describes the patient's respiratory state over time, gating may subdivide the acquired data into segments within which respiratory motion is small. Reconstructed gates have less respiratory motion artifacts, but the subdivision of counts reduces signal-to-noise ratio (SNR) in these images. For this reason, motion correction allowing use of more data per image is desirable, which, in turn, requires an estimation of the patient's respiratory motion. Correcting for respiratory motion is very difficult due to the high noise and low resolution of SPECT imaging.
Prevailing methods to estimate respiratory motion rely on reconstructing gated images and using 3D-3D registration or optical flow methods to estimate motion fields between these states. These fields may then be either applied directly to the gated reconstructions to deform and sum them or may be incorporated into the projection operator for a new motion corrected reconstruction. The computational runtime necessary to perform a series of independent reconstructions, as well as issues arising from the irregularity of patient breathing, make these methods inconsistent. Data from some gates may be count-deficient or completely missing at certain view angles, resulting in poor quality gated reconstructions that hinder the 3D-3D registration process.
A motion model may be used to reduce the number of estimable parameters needed to calculate motion characteristics and stabilize motion estimates. Due to the difficulty in determining necessary parameters, these models are usually either based on averages over groups of patients or trained using high quality 4-D computed tomography (CT) or magnetic resonance (MR) data acquired prior to the acquisition being motion corrected. The former case does not allow for inter-patient variations in respiratory motion and assumes that the same motion model is valid for all patients. The latter case assumes that a patient-specific model will be constant at two consecutive imaging time points, which may be days apart. Both cases are incapable of adapting to changes in respiration that may occur as the nuclear imaging acquisition progresses.
An alternative for cases where respiratory motion may be described sufficiently in the projection space is to estimate motion using a series of 2D-2D registrations at each projection view. These 2D motion fields may be applied directly to the data prior to reconstruction or passed to the projection operator. In either case, the issue of irregular breathing is mitigated, and computation time may be spared. Nuclear imaging suffers from image noise, and image noise may destabilize the motion estimates.